نشریه سامانه های غیرخطی در مهندسی برق
سال هفتم شماره 1 (پیاپی 11، بهار و تابستان 1399)

Sign language recognition systems help deaf people to access various media. In this paper, the Leap Motion Controller (LMC) and the image of the hand are exploited for sign language recognition. The LMC provides 3D position of the hand joints. The first set of features are extracted from the data provided by the LMC. When the hand is not located in vertical view of the LMC or when the hand posed like a fist, the precise position of the hand joints is not recognizable. The second feature extracted from hand image helps most hand gestures be recognized precisely. The second feature includes histogram of oriented gradients and the distance of the hand contour form the center of the hand. Also, a dataset composed of variant American sign language gestures is created which includes 64000 samples. In recognition stage, random forest is applied which is a good option for large datasets. The experimental results show that the proposed method performs better than similar methods.

The modeling of high frequency electromagnetic transients and the simulation of the voltage and the current distribution in the multi-winding traction transformerchr('39')s windings due to these transient waves are very important. In the present article, in addition of presenting finite element models, the coupled field-circuit approach is proposed for the modeling of high frequency electromagnetic transients in a multi-winding traction transformer. The proposed method uses two-dimensional finite element models coupled with an external circuit to model the electromagnetic transient behavior of the multi-winding traction transformer. Afterwards, the results of the presented method have been compared with the results obtained from a complete three-dimensional finite element model as well as the detail modelchr('39')s results and the results are validated. Finally, the validated high-frequency model has been used to study the impulse response of the transformer. As shown, the proposed approach is a simple and fast method, and also has good accuracy in modeling of the impulse voltage distribution in the multi-winding traction transformerchr('39')s windings.

In this paper, a new output feedback control method was used based on a linear matrix inequality to control the angular position of AC servo motor shaft. The proposed control method does not need to measure all of the AC servo motor statuses; it only uses the output feedback and is robust against the uncertain servo motor parameters and the disturbances applied to it. The proposed control method was compared in several scenarios with a Standard Internal Model Control-Sliding Mode Control (SIMC-SMC) method, 2-Degree-of-Freedom Internal Model Control-Sliding Mode Controller (2DOF-IMC-SMC) method, 2-Degree-of-Freedom Internal Model Control-Proportional-Integral-Derivative (2DOF-IMC-PID) method, Standard Internal Model Control-Proportional-Derivatives (SIMC-PD) method, and Internal Model Control-Proportional-Integral-Derivative-Extended State Observer (IMC-PID-ESO) method. The simulation results show that the proposed controller has desirable performance against disturbances and uncertain parameters of the AC servo motor compared with other mentioned controllers. This method relative to other controllers decreased the error of tracking the angular position of the servo motor to 30% .The simulation was performed in the Matlab Software.

Away to decrease the costs of generation and improve the performance of the grid generator, solving the problem of OPF based on line congestion management. As the power flow equation is nonlinear, this paper has performed the PSO algorithm to solve the OPF problem. By considering two technique this paper has performed the PSO algorithm for improving the performance. The first technique is to use a chaos generator to prevent PSO particles from sticking to local minimum points and the second is to consider the GSF in the WPSO algorithm structure so that the power passing through network lines can be simultaneously calculated and real power flow. Finally, the result of  WPSO-GSF algorithm which includes the bus voltage values, line losses, injection power to b buses, power passing through lines, total generation cost, setting electricity prices in two ways, UMP or LMP, depending on filling line capacity and calculating generatorschr('39') profits has carried out .and also, to check the accuracy of the algorithm, the proposed method has been tested on IEEE 14-BUS, 30-BUS, 57-BUS standard networks, the results which indicate an increase in the speed and accuracy of the WPSO-GSF algorithm compared to other methods in improving the OPF problem.

The very low cost of renewable energy resources and the increase of the greenhouse gas emissions and fuel cost have led to a simultaneous increase in utilizing the renewable energy resources (RESs) and electric vehicles (EVs). In this paper, a mixed-integer linear programming (MILP) model is proposed for the stochastic unit commitment problem with the aim of minimizing the operation cost and the emission in the presence of EVs and RESs. EVs with the capability of vehicle-to-grid (V2G) can operate as energy storage units in the smart grid and, if necessary, be connected to the network as generation resources. In this paper, an aggregator is responsible for coordinating the charging and discharging of EVs. The RESs uncertainties has complicated the management of electric vehicles and the unit commitment problem. Therefore, in this paper, Monte Carlo simulation method is used for modeling the uncertainties of the wind and solar power and the load demand. The simulation results show that the simultaneous utilization of the proposed MILP model and the probability distance method for reducing the number of scenarios, can minimize the operation cost of thermal units and pollutant emissions while reduces the solution time, significantly

With the increasing expansion of power systems, random factors affecting the performance of these systems have also increased. Rising demand for electrical energy, along with the aforementioned random factors, has led to uncertainty analysis methods being of particular importance in analyzing the small signal stability of the power systems. In this paper, a method based on polynomial approximation for probabilistic small signal stability analysis of the power systems is presented. Since the correct determination of unknown coefficients has a direct effect on the accuracy of the polynomial approximation method, this paper presents a method that is able to determine these coefficients with more coverage on the probable input space of the problem and in addition, is able to maintain its efficiency even by increasing the number of random input variables. After determining unknown coefficients, the load flow results and system state matrix are determined for random changes of all loads and based on Hermitchr('39')s polynomial approximation. Then, the eigenvalues ​​of the system are determined and the stability of the small signal of the system is probabilistically studied. In order to evaluate the accuracy and effectiveness of the proposed method, the IEEE 14-bus benchmark system is simulated in MATLAB software and the results of the proposed method is compared with the results of the two conventional methods of Point Estimation and Monte Carlo. Examination of the results has shown that the proposed method in this paper, in addition to validity, has good accuracy and high computational speed.

In this paper, a real-time interactive high resolution soft tissue modeling is implemented that enriches a coarse model in a data-driven approach to produce a fine model. As a preprocess step, a set of corresponding coarse and fine models are simulated for the database. In the test step, by using a regressor, the coarse model in the test set is compared to the coarse models in the training set and the blending weights are assigned to the training coarse models. These weights are used for approximating the fine model as a linear combination of the corresponding fine models in the train set. To decrease the computational complexity, assuming that applying a force on the tissue results in a local deformation, a feature extraction algorithm is proposed that considers the displacements of the contact node and its neighbor nodes and ignores the rest. This results in a low dimensional feature vector and decreases the computational complexity. In order to compute the blending weights, a nonlinear regressor with Gaussian kernel is leveraged. To eliminate the artefacts resulting from negative weights, a nonnegative least square algorithm is used for regression. Simulation results of applying the proposed method on two soft tissue models are investigated regarding the reconstruction accuracy, computational complexity and running time.